Resultados de investigación

Junta de Andalucía, Grupo de Investigación PAI-HUM-0159; Archivo Arturo Reyes

Junta de Andalucía, Grupo de Investigación PAI-HUM-0159; Archivo Arturo Reyes

Junta de Andalucía, Grupo de Investigación PAI-HUM-0159; Archivo Arturo Reyes

Junta de Andalucía, Grupo de Investigación PAI-HUM-0159; Archivo Arturo Reyes

Mauerer, Ingrid, 2019, "Replication Data for: The Neglected Role and Variability of Party Intercepts in the Spatial Valence Approach", https://doi.org/10.7910/DVN/PIV1KX, Harvard Dataverse, V1

We have examined the effect of an acute stress in the proteomic profile of hippocampus. For this purpose, to extract information from proteomic profiles (1 and 24 hours post-acute stress) a Quadrupole-Orbitrap mass spectrometry (Q-Orbitrap-MS) was used for the massive identification and quantification of hippocampal proteins. After collecting the raw data extracted from this technique, for each proteomic profile, a functional enrichment study was performed. Firstly, the significantly overexpressed and underexpressed hippocampal proteins of the right hemisphere of experimental groups with their respective controls (stress/control) were selected. Then, using STRING, the number of interactors was increased to 100 for both over- and under-expressed proteins. Furthermore, MCODE was used as a clustering tool for the protein‒protein interaction network obtained from STRING. After that, a functional enrichment analysis was performed using DAVID by introducing each protein gene name grouped into the clusters from the previous step. Biological processes in each protein clustering were determined as well as the function it is involved in. Finally, a network analysis of the GO terms obtained from DAVID was performed so that it was easier to interpret and integrate the biological implications of the results. Data were validate by using a western blot analysis. In the end, the main conclusion was that this short-term stressor altered the protein profile of the hippocampus, changing the protein abundance after 1 hour or 24 hours of stress application., - Ministerio de Ciencia e Innovación – Plan Nacional I+D+I from Spain (PID2020-117464RB-I00) to CP and MP-M; - FEDER/Ministerio de Ciencia e Innovación – Agencia Estatal de Investigación Investigación from Spain (PSI2017-83408-P) to CP - FEDER/Junta de Andalucía – Proyectos I+D+I en el marco del Programa Operativo FEDER Andalucía 2014-2020 (UMA20-FEDERJA-112) to CP and MP-M - Consejería de Conocimiento, Investigación y Universidades, Junta de Andalucía (P20_00460) to CP. - Ministerio de Universidades from Spain: FPU16/05308 to AN-Q and FPU19/03629 to MII-L.

Datos pertenecientes a un estudio instrumental de las Escalas de Afecto Negativo y Positivo (NAPAS) en mujeres con diagnóstico de cáncer de mama.

How do identities affect our political perceptions? Drawing on the social identity theory, we offer a new notion and empirical modeling strategy to study divergence in party placement perceptions. Our framework builds on the idea that membership in social groups has the potential to act as a social identity by systematically structuring perceptual divergence among groups. We conceptualize two consequences of social identity formation, ingroup favoritism and outgroup hostility: an ingroup effect brings a party closer to the preferred policy of the group; an outgroup effect pushes the party away. The approach permits detecting these effects and pinpoints the group-based characteristics producing perceptual polarization in multiparty systems using standard public opinion survey data. An application to the Basque region of Spain shows that over two decades, social identities around national sentiments and religion have produced the most perceptual divergence and polarization, whereas gender or social class do not structure party perceptions in the region., Program EMERGIA (EMC21-00256), Junta de Andalucía,, ID2020-114309GB-I00 financed by MCIN/AEI/10.13039/501100011033

Radio access network optimization is a critical task in current cellular systems. For this purpose, Minimization of Drive Test (MDT) functionality provides mobile operators with georeferenced network performance statistics to tune radio propagation models in re-planning tools. However, some samples in MDT traces contain critical location errors due to the user equipment's energy-saving, thus making MDT data filtering vital to guarantee an adequate performance of MDT-driven algorithms. Supervised Learning (SL) allows to train automatic systems for detecting abnormal MDT measurements by using a labeled dataset. Unfortunately, labeling MDT data is a labor-intensive task, that can be alleviated by using Self-Supervised Learning (SSL). This work presents a novel SSL method to detect MDT measurements with abnormal position information in road scenarios. For this purpose, a dataset is first labeled by combining unlabeled MDT traces from high-mobility users and freely available land use maps, and then an SL classifier is trained. Model assessment is carried out using MDT data collected in a live Long-Term Evolution (LTE) network. Performance analysis includes the comparison of six well-known SL algorithms and 3 different sets of input features aiming to improve model accuracy, generalizability, and explainability, respectively. Results show that considering predictors regarding positioning error increases model accuracy, whereas omitting this information allows to cover a wider range of terminals. Likewise, Shapley Additive exPlanations (SHAP) analysis proves that the use of high-level predictors significantly improves model explainability., Proyecto 6G-GRADA, Ayuda para la formacion de doctores PRE2022-102119, Agencia Estatal de Investigacion, Unión Europea

Datos en Excel pertenecientes a una investigación sobre satisfacción vital en adolescentes ecuatorianos